Search Results - "Itziar Gonzalez-Dios"

Natural Language Processing and Language Technologies for the Basque Language by Gonzalez-Dios, Itziar, Altuna, Begoña

“…The presence of a language in the digital domain is crucial for its survival, as online communication and digital language resources have become the standard…”
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The corpus of Basque simplified textx (CBST) by Gonzalez-Dios, Itziar, Aranzabe, María Jesús, de Ilarraza, Arantza Díaz

“…In this paper we present the corpus of Basque simplified texts. This corpus compiles 227 original sentences of science popularisation domain and two simplified…”
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The corpus of Basque simplified texts (CBST) by Gonzalez-Dios, Itziar, Aranzabe, María Jesús, Díaz de Ilarraza, Arantza

“…In this paper we present the corpus of Basque simplified texts. This corpus compiles 227 original sentences of science popularisation domain and two simplified…”
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Easy-to-Read in Germany: A Survey on its Current State and Available Resources by Madina, Margot, Gonzalez-Dios, Itziar, Siegel, Melanie

“…Easy-to-Read Language (E2R) is a controlled language variant that makes any written text more accessible through the use of clear, direct and simple language…”
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MultiAzterTest: a Multilingual Analyzer on Multiple Levels of Language for Readability Assessment by Bengoetxea, Kepa, Gonzalez-Dios, Itziar

“…Readability assessment is the task of determining how difficult or easy a text is or which level/grade it has. Traditionally, language dependent readability…”
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Automatic Text Simplification: State of Art by Gonzalez-Dios, Itziar, Aranzabe, Maria Jesus, Diaz de Ilarraza, Arantza

“…The aim of this paper is to give an overview of the state-of-art in automatic text simplification. To that end, we present the systems and prototypes according…”
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A Hard Nut to Crack: Idiom Detection with Conversational Large Language Models by Fornaciari, Francesca De Luca, Altuna, Begoña, Gonzalez-Dios, Itziar, Melero, Maite

“…In this work, we explore idiomatic language processing with Large Language Models (LLMs). We introduce the Idiomatic language Test Suite IdioTS, a new dataset…”
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This is not a Dataset: A Large Negation Benchmark to Challenge Large Language Models by García-Ferrero, Iker, Altuna, Begoña, Álvez, Javier, Gonzalez-Dios, Itziar, Rigau, German

“…Although large language models (LLMs) have apparently acquired a certain level of grammatical knowledge and the ability to make generalizations, they fail to…”
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Multilingual Central Repository: a Cross-lingual Framework for Developing Wordnets by Guinovart, Xavier Gómez, Gonzalez-Dios, Itziar, Oliver, Antoni, Rigau, German

“…Language resources are necessary for language processing,but building them is costly, involves many researches from different areas and needs constant…”
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Commonsense Reasoning Using WordNet and SUMO: a Detailed Analysis by Álvez, Javier, Gonzalez-Dios, Itziar, Rigau, German

“…We describe a detailed analysis of a sample of large benchmark of commonsense reasoning problems that has been automatically obtained from WordNet, SUMO and…”
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Noisy Channel for Automatic Text Simplification by Cumbicus-Pineda, Oscar M, Gutiérrez-Fandiño, Iker, Gonzalez-Dios, Itziar, Soroa, Aitor

“…In this paper we present a simple re-ranking method for Automatic Sentence Simplification based on the noisy channel scheme. Instead of directly computing the…”
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Textual Entailment for Event Argument Extraction: Zero- and Few-Shot with Multi-Source Learning by Sainz, Oscar, Gonzalez-Dios, Itziar, de Lacalle, Oier Lopez, Min, Bonan, Agirre, Eneko

“…Recent work has shown that NLP tasks such as Relation Extraction (RE) can be recasted as Textual Entailment tasks using verbalizations, with strong performance…”
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Applying the Closed World Assumption to SUMO-based FOL Ontologies for Effective Commonsense Reasoning by Álvez, Javier, Gonzalez-Dios, Itziar, Rigau, German

“…Most commonly, the Open World Assumption is adopted as a standard strategy for the design, construction and use of ontologies. This strategy limits the…”
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Validating WordNet Meronymy Relations using Adimen-SUMO by Álvez, Javier, Gonzalez-Dios, Itziar, Rigau, German

“…In this paper, we report on the practical application of a novel approach for validating the knowledge of WordNet using Adimen-SUMO. In particular, this paper…”
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Testuen sinplifikazio automatikoa: arloaren egungo egoera by Itziar Gonzalez-Dios, María Jesús Aranzabe, Arantza Díaz de Ilarraza

“…Lan honen helburua testuen sinplifikazio automatikoaren arloaren egungo egoera aurkeztea da. Horretarako, ikerketa-lerro honetan egindako sistemak eta…”
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The BigScience ROOTS Corpus: A 1.6TB Composite Multilingual Dataset by Laurençon, Hugo, Saulnier, Lucile, Wang, Thomas, Akiki, Christopher, del Moral, Albert Villanova, Scao, Teven Le, Von Werra, Leandro, Mou, Chenghao, Ponferrada, Eduardo González, Nguyen, Huu, Frohberg, Jörg, Šaško, Mario, Lhoest, Quentin, McMillan-Major, Angelina, Dupont, Gerard, Biderman, Stella, Rogers, Anna, allal, Loubna Ben, De Toni, Francesco, Pistilli, Giada, Nguyen, Olivier, Nikpoor, Somaieh, Masoud, Maraim, Colombo, Pierre, de la Rosa, Javier, Villegas, Paulo, Thrush, Tristan, Longpre, Shayne, Nagel, Sebastian, Weber, Leon, Muñoz, Manuel, Zhu, Jian, Van Strien, Daniel, Alyafeai, Zaid, Almubarak, Khalid, Vu, Minh Chien, Gonzalez-Dios, Itziar, Soroa, Aitor, Lo, Kyle, Dey, Manan, Suarez, Pedro Ortiz, Gokaslan, Aaron, Bose, Shamik, Adelani, David, Phan, Long, Tran, Hieu, Yu, Ian, Pai, Suhas, Chim, Jenny, Lepercq, Violette, Ilic, Suzana, Mitchell, Margaret, Luccioni, Sasha Alexandra, Jernite, Yacine

“…As language models grow ever larger, the need for large-scale high-quality text datasets has never been more pressing, especially in multilingual settings. The…”
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BLOOM: A 176B-Parameter Open-Access Multilingual Language Model by Scao, Teven Le, Fan, Angela, Gallé, Matthias, Webson, Albert, Wang, Thomas, Bekman, Stas, Laurençon, Hugo, Launay, Julien, Raffel, Colin, Simhi, Adi, Alfassy, Amit, Rogers, Anna, Leong, Colin, van Strien, Daniel, Ponferrada, Eduardo González, Levkovizh, Efrat, Benyamina, Hamza, Tran, Hieu, Yu, Ian, Johnson, Isaac, Bhattacharjee, Joydeep, Von Werra, Leandro, Dey, Manan, Jiang, Mike Tian-Jian, Jauhar, Mohammad A, Kassner, Nora, Pyysalo, Sampo, Pai, Suhas, Schick, Timo, Thrush, Tristan, Nikoulina, Vassilina, Laippala, Veronika, Heinzerling, Benjamin, Taşar, Davut Emre, Salesky, Elizabeth, Lee, Wilson Y, Szczechla, Eliza, Chhablani, Gunjan, Wang, Han, Rozen, Jos, Manica, Matteo, Nayak, Nihal, Teehan, Ryan, Albanie, Samuel, Shen, Sheng, Ben-David, Srulik, Kim, Taewoon, Neeraj, Trishala, Roberts, Adam, Tae, Jaesung, Phang, Jason, Press, Ofir, Ryabinin, Max, Peyrounette, Myriam, Patry, Nicolas, Cornette, Pierre, Dettmers, Tim, Ligozat, Anne-Laure, Névéol, Aurélie, Taktasheva, Ekaterina, Kalo, Jan-Christoph, Clive, Jordan, Kim, Najoung, Mirkin, Shachar, Pais, Shani, Pruksachatkun, Yada, Pestana, Amanda, Faranak, Amy, Santos, Ana, HajiHosseini, Azadeh, Ajibade, Benjamin, Saxena, Bharat, Nguyen, Duong A, Rezanejad, Habib, Bhattacharya, Indrani, Nejadgholi, Isar, McKenna, Michael, Burynok, Mykola, Rajani, Nazneen, Samuel, Olanrewaju, Kromann, Rasmus, Shubber, Sarmad, Viguier, Sylvain, Miranda-Escalada, Antonio, Singh, Ayush, Manjavacas, Enrique, Barth, Fabio, Bulchandani, Lokesh, Nezhurina, Marianna, Liu, Minna, Kang, Myungsun, Dahlberg, Nathan, Chandrasekhar, Ramya, Eisenberg, Renata, Canalli, Rodrigo, Schweter, Stefan, Laud, Tanmay, Kainuma, Tomoya, Venkatraman, Yash, Xu, Yingxin

“…Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these…”
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